| <p>The most recent interface is OBD-II, which was introduced to standardize the physical connector, its pinout, the signaling protocols and the format of the messages they deal with. The system is usually employed to monitor and regulate gas emissions and must be present in all cars produced since 1996 in Europe and United States. The OBD interface also helps aftermarket maintenance services when tracing the origin of mechanical problems since it stores engine fault codes that provide mechanics with information about problems and their sources. The collection process uses the OBD-II interface as the means of accessing the vehicle’s data, transferring them via the Bluetooth connection to a smartphone with the Android operating system, where the data is processed and registered through an app.</p> |
| <p><span class="citation">P. H. Rettore, André, et al. (2016)</span><a href="http://ieeexplore.ieee.org/abstract/document/7795542/">link</a>: Guided us to better understand vehicular data after processing it. This work lead us to eliminate and treat data problems such as outliers, conflict, incompleteness, ambiguity, correlation, and disparateness.</p> |
| <p><span class="citation">Cunha et al. (2017)</span><a href="http://homepages.dcc.ufmg.br/~fdcunha/MinicursoTextoV2.pdf">link</a>: In this mini-course, the objective is to discuss ITS, presenting an overview of the area, its challenges, and opportunities. In this way, this mini-course will introduce the main concepts involved in the ITS architecture, its implementation and integration with other computer networks, and how to evaluate its performance. We will also show the main applications in the literature that cooperate for the existence of ITS. In the end, we will discuss the challenges and opportunities found in the areas of interest of the SBRC symposium, among which we highlight: data collection and fusion, characterization, prediction, security and privacy.</p> |
| <p><span class="citation">Rettore et al. (2018)</span>: This work explores the driver identification as an extra authentication factor to local services and vehicular networks. Then, a virtual sensor was developed to determine the driver identity, with precision above 98%, using embedded sensor data. This sensor was also used to identify driver suspects. Besides, based on the suspect identification, we discussed the impacts of these drivers in the data dissemination in a vehicular network.</p> |
| <p>Cunha, Felipe Domingos da, Guilherme Maia, Clayson Celes, Daniel Guidoni, Fernanda de Souza, Heitor Ramos, and Leandro Villas. 2017. “Sistemas de Transporte Inteligentes: Conceitos, Aplicações e Desafios.” In <em>SBRC 2017 - Minicursos ()</em>. <a href="http://homepages.dcc.ufmg.br/~fdcunha/MinicursoTextoV2.pdf" class="uri">http://homepages.dcc.ufmg.br/~fdcunha/MinicursoTextoV2.pdf</a>.</p> |
| <p>Rettore, Paulo Henrique, Bruno Pereira Santos André, Campolina, Leandro Aparecido Villas, and Antonio A.F. Loureiro. 2016. “Towards Intra-Vehicular Sensor Data Fusion.” In <em>Advanced Perception, Machine Learning and Data Sets (Amd’16) as Part of the 2016 Ieee 19th International Conference on Intelligent Transportation Systems (Itsc 2016)</em>. Rio de Janeiro.</p> |
| <p>Rettore, Paulo Henrique, André Campolina, Artur Luis de Souza, Guilherme Maia, Leandro Aparecido Villas, and Antonio A.F. Loureiro. 2018. “Driver Authentication in VANETs Based on Intra-Vehicular Sensor Data.” In <em>2018 Ieee Symposium on Computers and Communications (Iscc) (Iscc 2018)</em>. Natal, Brazil.</p> |